Partial differential equations are a powerful tool for modeling many real-world phenomena. When derived from the first principles, partial different equations can serve as predictive models which do not require any data for tuning. When learned from data [1]}, they often outperform other models by incorporating the inductive bias of the continuity of the modeled domain (time or space). Inference in this type of models is done by solving partial differential equations, that is by finding a trajectory that satisfies the model equations and a set of initial and boundary conditions. Since analytic solutions exist only for a limited number of models (most likely derived from the first principles), inference is almost inevitably done by numerical solvers of differential equations.
